Internet-based quality digital management platform

Abstract

The invention relates to the technical field of digital quality management, in particular to an internet-based quality digital management platform, which comprises a design quality control module, a production quality analysis module, a customer feedback analysis module and a management feedback module, according to the invention, digital management is carried out on the product design quality through data acquisition and analysis, design files in various formats, including image files, description files, review and modification suggestions and modification records, are obtained from the Internet and are comprehensively analyzed, so that comprehensive, systematic and efficient management on the product design quality is realized; the material quality is comprehensively analyzed in combination with the aspects of mechanical properties, residual stress and the like, so that an enterprise can screen out materials with excellent performance; by accessing the customer feedback analysis module, sales records, return visit records, complaint records and return and repair record data in different formats are integrated, and powerful support is provided for product improvement and service optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of digital quality management, and in particular to an Internet-based digital quality management platform. Background Art

[0002] Digital quality management is a new development stage of the quality management model. It emphasizes dynamic quality, all quality can be quantified, and implements maximum standardization and digitization of all quality-related activities around the entire business chain. Digital quality management technology realizes in-depth mining and analysis of quality data, providing more accurate and timely decision-making support for quality management. At the same time, the platform also focuses on the interconnection and system integration of all links in the product life cycle, promotes information sharing and coordination among departments, and improves response speed and production efficiency.

[0003] However, the existing digital quality management technology still has many problems. First, there is a lack of effective coordination mechanism between departments within the enterprise, and many business processes and data transmission have not yet been fully informatized, resulting in serious information islands.

[0004] Secondly, the digital quality management of many enterprises still relies on manual data entry, which is lagging and has errors. At the same time, there are difficulties and blind spots in data processing technology, and some data are uploaded in the form of pictures, which affects the subsequent targeted data analysis and makes it difficult to support subsequent data analysis and decision support.

[0005] In view of the above problems, it is necessary to propose an Internet-based digital quality management platform. Summary of the invention

[0006] The purpose of the present invention is to solve the problems existing in the background technology and to propose a quality digital management platform based on the Internet.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A quality digital management platform based on the Internet, including a design quality control module, a production quality analysis module, a customer feedback analysis module and a management feedback module.

[0009] The design quality control module digitally manages product design quality through data collection and analysis, and obtains a collection of product design files through the Internet, including: image files in DWG, DXF, PDF and JPEG formats; description files in DOC, PDF and TXT formats; review records and modification records in DOC, PDF, XML and TXT formats.

[0010] For the image file, extract the geometric feature information in the six views and import it into the finite element analysis software. Match the extracted geometric feature information with the target shape based on the shape matching algorithm, build a three-dimensional geometric model of the product, and perform image enhancement, mesh repair, shape matching and noise removal. Obtain the volume V of the three-dimensional geometric model. Construct an XYZ three-dimensional coordinate system in the three-dimensional geometric model.

[0011] For the aforementioned description documents and review records, extract the natural language containing specific modification opinions and convert it into machine language. Perform relationship extraction, text distribution, and collation and merging on the converted machine language to obtain the following types of review questions: Type 1: Review questions for violations of general design specifications; Type 2: Modification opinions for violations of mandatory provisions; Type 3: Review questions for dimensioning errors; Type 4: Review questions for safety hazards; Type 5: Review questions for confusing structural logic; Type 6: Review questions for unclear text descriptions. Match each type of review question to a corresponding modification parameter Ki. Where i is the number symbol of the review question.

[0012] As a preferred method of the present invention, the drawing name, drawing number and specific pointing position in the image file pointed to by each type of review question are obtained and mapped to the three-dimensional geometric model, so as to obtain the positioning coordinates of the corresponding points of each review question in the three-dimensional geometric model i(xi, yi, zi) = (x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn), where n is the total number of positioning coordinates of the review question.

[0013] For the modification record, obtain the number of design modifications N1 and the number of modifications N2 that have passed re-examination after modification.

[0014] As a preferred method of the present invention, the positioning coordinates i (xi, yi, zi) = (x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn) are taken as the center of the circle, and a sphere is constructed with a preset detection radius r, and the number of other positioning coordinates in the sphere is obtained, which is recorded as the density feature number Count(i) of the positioning coordinate i.

[0015] By formula Calculate the defect density index E1, where r is the preset detection radius, D is the volume domain of the intersection, that is, the intersection of the sphere constructed with the preset detection radius r and the three-dimensional geometric model, V(i) is the volume of the intersection, and S is the volume domain of the entire three-dimensional geometric model.

[0016] The review pass rate E2 is calculated by the formula E2=K1×N1 / n+K2×N2 / n, where K1 and K2 are preset weight factors.

[0017] The production quality analysis module obtains the product material list of the input assembly line and performs natural language processing to convert it into machine language. Relationship extraction, text distribution, and sorting and merging are performed on the converted machine language to generate a material data structure table. The material types contained in the material data structure table are numbered with the numbering symbol j, j = 1, 2, 3, ..., m; m is the total number of material types. The mechanical performance parameters of the specimens corresponding to each material obtained in the mechanical test are obtained, including yield tensile strength A1j, compressive strength A2j, elongation A3j, Poisson's ratio A4j, elastic modulus A5j, and yield strength ratio A6j. Through the formula Calculate the mechanical property characteristic parameter E3(j) of each material type j. Where λp is a set of preset weight factors; APj' is a set of preset mechanical property parameter standard values; where p = 1, 2, 3, 4, 5, 6.

[0018] As a preferred embodiment of the present invention, the impurity component ratio B1 obtained in the chemical composition analysis of the sample corresponding to each material is obtained based on atomic spectroscopy, and the maximum processing speed B2, the maximum processing temperature B3 and the minimum processing temperature B4 of the sample corresponding to each material type in the hot processing test are obtained. The reliability characteristic parameter E4(j) of each material type j is calculated by the formula E4(j)=qj×B1×B2×exp(B4-B3). Where qj is the preset weight factor corresponding to each material type j.

[0019] Access the experimental records in the X-ray stress tester, and retrieve the diffraction angle θ0(j) of the sample of each material type j in the stress-free state, as well as the diffraction angle θ(j) corresponding to the diffraction peak after the hot working test. Calculate the residual stress characteristic value of each material type j where A4j is Poisson's ratio, A5j is the elastic modulus, and Ψ is the angle between the measurement direction and the stress direction.

[0020] The customer feedback analysis module obtains market feedback data, including sales records, return visit records, complaint records, and return repair records in DOC, PDF, XML, and TXT formats. Extract elements and conduct quantitative analysis from the market feedback data to obtain the total number of sales orders w. Obtain the total number of complaints u and number each customer complaint with the number symbol r, r = 1, 2, 3, ..., u. Obtain the type of product problem for each customer complaint, including: technical support issues, packaging issues, quality issues, functional issues, logistics safety issues, and installation issues. Each product problem type corresponds to a preset impact factor g.

[0021] Obtain the processing time Tr(r) and customer satisfaction Pr(r) after processing for each customer complaint r.

[0022] Get the first processing and resolution result of each customer complaint r. If the result is to resolve the customer complaint, set the first resolution status code Solve(r) to 1; otherwise, set the first resolution status code Solve(r) to 0. Get the customer's repurchase status after each complaint r. If the customer repurchases within the preset time T after the complaint, set the repurchase status code Buy-Back(r) to 1; otherwise, set the repurchase status code Buy-Back(r) to 0.

[0023] As a preferred embodiment of the present invention, by formula Calculate the maintenance cost characteristic value E5 and the maintenance effectiveness characteristic value E6.

[0024] The management feedback module receives the defect density index E1 and review pass rate E2 from the design quality control module; it receives the material quality index E3(j), reliability characteristic parameter E4(j) and residual stress characteristic value of each material type j from the production quality analysis module. Receive the customer maintenance cost characteristic value E5 and maintenance efficiency characteristic value E6 sent by the customer feedback analysis module.

[0025] When the defect density index E1 is greater than the preset threshold value E1max or the review pass rate E2 is greater than the preset threshold value E2Max, the product design quality is judged to be poor;

[0026] When the material quality index E3(j) is less than the preset threshold E3min or the reliability characteristic parameter E4(j) is less than the preset threshold E4max or the residual stress characteristic value When it is greater than the preset threshold, it is determined that the product production material, that is, material type j, has performance problems;

[0027] When the maintenance cost characteristic value E5 is greater than the preset threshold value E5Max, it is determined that the product quality control is insufficient and the after-sales customer service maintenance cost is too high;

[0028] When the maintenance effectiveness characteristic value E6 is less than the preset threshold value E6Min, it is determined that the after-sales customer service level is too low.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] (1) Digitally manage product design quality through data collection and analysis, obtain design files in various formats from the Internet, including image files, description files, review and modification opinions, and modification records; extract natural language containing specific modification opinions from description files and review and modification opinions, and convert them into machine language; extract geometric feature information from six views of image files, and import them into finite element analysis software to build a three-dimensional geometric model of the product; expand data collection methods, making the evaluation and management of design quality more systematic and efficient; achieve comprehensive, systematic and efficient management of product design quality through digital management, three-dimensional modeling and precise analysis, intelligent processing of review and modification opinions, and calculation of defect density index;

[0031] (2) The present invention combines the natural language processing of the product material list with the production quality analysis module and converts it into machine language, thereby improving the automation of data processing and reducing the errors and time costs of manual input; by automatically performing relationship extraction, text distribution and sorting and merging, a material data structure table is generated, making the data analysis process more efficient and accurate; and by combining mechanical properties, residual stress and other aspects to conduct a comprehensive analysis of material quality, it helps enterprises to screen out materials with excellent performance and provide strong support for product design and production;

[0032] (3) The present invention accesses the customer feedback analysis module and integrates sales records, return visit records, complaint records and return and repair records from different formats, thereby providing rich materials for subsequent data analysis. By element extraction and quantitative analysis, key indicators such as the total number of sales orders and the total number of complaints are obtained, which helps enterprises understand market trends and changes in customer demand, and provides strong support for product improvement and service optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings:

[0034] Figure 1 is a system block diagram of the present invention;

[0035] Figure 2 It is a logic judgment flow chart of the present invention. DETAILED DESCRIPTION

[0036] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1As shown, an Internet-based digital quality management platform includes a design quality control module, a production quality analysis module, a customer feedback analysis module and a management feedback module.

[0038] The design quality control module digitally manages product design quality through data collection and analysis, and obtains a collection of product design files through the Internet, including: image files in DWG, DXF, PDF and JPEG formats; description files in DOC, PDF and TXT formats; review records and modification records in DOC, PDF, XML and TXT formats.

[0039] For the image file, extract the geometric feature information in the six views and import it into the finite element analysis software. Match the extracted geometric feature information with the target shape based on the shape matching algorithm, build a three-dimensional geometric model of the product, and perform image enhancement, mesh repair, shape matching and noise removal. Obtain the volume V of the three-dimensional geometric model. Construct an XYZ three-dimensional coordinate system in the three-dimensional geometric model.

[0040] For the aforementioned description documents and review records, extract the natural language containing specific modification opinions and convert it into machine language. Perform relationship extraction, text distribution, and collation and merging on the converted machine language to obtain the following types of review questions: Type 1: Review questions for violations of general design specifications; Type 2: Modification opinions for violations of mandatory provisions; Type 3: Review questions for dimensioning errors; Type 4: Review questions for safety hazards; Type 5: Review questions for confusing structural logic; Type 6: Review questions for unclear text descriptions. Match each type of review question to a corresponding modification parameter Ki. Where i is the number symbol of the review question.

[0041] See also Figure 2 As shown, the drawing name, drawing number and specific pointing position in the image file pointed to by each type of review question are obtained and mapped to the three-dimensional geometric model, and the positioning coordinates of the corresponding points of each review question in the three-dimensional geometric model are obtained i(xi, yi, zi) = (x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn), where n is the total number of positioning coordinates of the review question.

[0042] For the modification record, obtain the number of design modifications N1 and the number of modifications N2 that have passed re-examination after modification.

[0043] Furthermore, a sphere is constructed with the positioning coordinates i (xi, yi, zi) = (x1, y1, z1), (x2, y2, z2), ..., (xn, yn, zn) as the center and a preset detection radius r, and the number of other positioning coordinates in the sphere is obtained, which is recorded as the density feature number Count(i) of the positioning coordinate i.

[0044] By formula Calculate the defect density index E1, where r is the preset detection radius, D is the volume domain of the intersection, that is, the intersection of the sphere constructed with the preset detection radius r and the three-dimensional geometric model, V(i) is the volume of the intersection, and S is the volume domain of the entire three-dimensional geometric model.

[0045] The review pass rate E2 is calculated by the formula E2=K1×N1 / n+K2×N2 / n, where K1 and K2 are preset weight factors.

[0046] The production quality analysis module obtains the product material list of the input assembly line and performs natural language processing to convert it into machine language. Relationship extraction, text distribution, and sorting and merging are performed on the converted machine language to generate a material data structure table. The material types contained in the material data structure table are numbered with the numbering symbol j, j = 1, 2, 3, ..., m; m is the total number of material types. The mechanical performance parameters of the specimens corresponding to each material obtained in the mechanical test are obtained, including yield tensile strength A1j, compressive strength A2j, elongation A3j, Poisson's ratio A4j, elastic modulus A5j, and yield strength ratio A6j. Through the formula Calculate the mechanical property characteristic parameter E3(j) of each material type j. Where λp is a set of preset weight factors; APj' is a set of preset mechanical property parameter standard values; where p = 1, 2, 3, 4, 5, 6.

[0047] Furthermore, based on the atomic spectroscopy method, the impurity component ratio B1 obtained in the chemical composition analysis of the sample corresponding to each material is obtained, and the maximum processing speed B2, the maximum processing temperature B3 and the minimum processing temperature B4 of the sample corresponding to each material type in the hot processing test are obtained. The reliability characteristic parameter E4(j) of each material type j is calculated by the formula E4(j)=qj×B1×B2×exp(B4-B3). Where qj is the preset weight factor corresponding to each material type j.

[0048] Access the experimental records in the X-ray stress tester, and retrieve the diffraction angle θ0(j) of the sample of each material type j in the stress-free state, as well as the diffraction angle θ(j) corresponding to the diffraction peak after the hot working test. Calculate the residual stress characteristic value of each material type j where A4j is Poisson's ratio, A5j is the elastic modulus, and Ψ is the angle between the measurement direction and the stress direction.

[0049] The customer feedback analysis module obtains market feedback data, including sales records, return visit records, complaint records, and return repair records in DOC, PDF, XML, and TXT formats. Extract elements and conduct quantitative analysis from the market feedback data to obtain the total number of sales orders w. Obtain the total number of complaints u and number each customer complaint with the number symbol r, r = 1, 2, 3, ..., u. Obtain the type of product problem for each customer complaint, including: technical support issues, packaging issues, quality issues, functional issues, logistics safety issues, and installation issues. Each product problem type corresponds to a preset impact factor g.

[0050] Obtain the processing time Tr(r) and customer satisfaction Pr(r) after processing for each customer complaint r.

[0051] Get the first processing and resolution result of each customer complaint r. If the result is to resolve the customer complaint, set the first resolution status code Solve(r) to 1; otherwise, set the first resolution status code Solve(r) to 0. Get the customer's repurchase status after each complaint r. If the customer repurchases within the preset time T after the complaint, set the repurchase status code Buy-Back(r) to 1; otherwise, set the repurchase status code Buy-Back(r) to 0.

[0052] Furthermore, through the formula Calculate the maintenance cost characteristic value E5 and the maintenance effectiveness characteristic value E6.

[0053] It should be noted that the larger the specific value of the maintenance cost characteristic value E5, the higher the manpower and material costs spent on after-sales customer service and customer maintenance; the larger the specific value of the maintenance efficiency characteristic value E6, the higher the level of customer loyalty maintained through after-sales customer service and customer maintenance.

[0054] The management feedback module receives the defect density index E1 and review pass rate E2 from the design quality control module; it receives the material quality index E3(j), reliability characteristic parameter E4(j) and residual stress characteristic value of each material type j from the production quality analysis module. Receive the customer maintenance cost characteristic value E5 and maintenance efficiency characteristic value E6 sent by the customer feedback analysis module.

[0055] When the defect density index E1 is greater than the preset threshold value E1max or the review pass rate E2 is greater than the preset threshold value E2Max, the product design quality is judged to be poor;

[0056] When the material quality index E3(j) is less than the preset threshold E3min or the reliability characteristic parameter E4(j) is less than the preset threshold E4max or the residual stress characteristic value When it is greater than the preset threshold, it is determined that the product production material, that is, material type j, has performance problems;

[0057] When the maintenance cost characteristic value E5 is greater than the preset threshold value E5Max, it is determined that the product quality control is insufficient and the after-sales customer service maintenance cost is too high;

[0058] When the maintenance effectiveness characteristic value E6 is less than the preset threshold value E6Min, it is determined that the after-sales customer service level is too low.

[0059] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0060] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and the claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations;

[0061] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An Internet-based digital quality management platform, including a design quality control module, characterized in that: The design quality control module obtains a set of product design files through the Internet, including image files, description files, review records and modification records; constructs a three-dimensional geometric model for the image files; extracts natural language containing specific modification opinions for the description files and review records and converts them into machine language; extracts relations, distributes texts, and organizes and merges the converted machine language to match it to a preset review problem type, and matches each type of review problem to a corresponding modification parameter Ki, where i is the number symbol of the review problem; Obtain the drawing name, drawing number and specific pointing position in the image file pointed to by each type of review question and map them to the three-dimensional geometric model to obtain the positioning coordinates i (xi, yi, zi) of the corresponding point of each review question in the three-dimensional geometric model; construct a sphere with the positioning coordinate i (xi, yi, zi) as the center and the preset detection radius r, and obtain the number of other positioning coordinates in the sphere, which is recorded as the density feature number Count(i) of the positioning coordinate i; through the formula Calculate the defect density index E1, where r is the preset detection radius, D is the volume domain of the intersection, that is, the intersection of the sphere constructed with the preset detection radius r and the three-dimensional geometric model, V(i) is the volume of the intersection, and S is the volume domain of the entire three-dimensional geometric model; for the modification record, obtain the number of design modifications N1 and the number of modifications that passed the re-examination after modification N2; calculate the review pass rate E2 by the formula E2=K1×N1 / n+K2×N2 / n, where K1 and K2 are preset weight factors.

2. The Internet-based digital quality management platform according to claim 1, characterized in that: It also includes production quality analysis module, customer feedback analysis module and management feedback module: The production quality analysis module obtains the product material list of the input assembly line and performs natural language processing to convert it into machine language; extracts relations, distributes texts, and organizes and merges the converted machine language to generate a material data structure table; numbers the material types contained in the material data structure table, and the numbering symbol is j, j = 1, 2, 3, ..., m; m is the total number of material types; obtains the mechanical performance parameters of the samples corresponding to each material obtained in the mechanical test, and obtains the mechanical performance characteristic parameters, reliability characteristic parameters and residual stress characteristic values ​​through calculation; The customer feedback analysis module obtains market feedback data, including sales records, return visit records, complaint records, and return repair records; extracts elements and conducts quantitative analysis from the market feedback data to obtain the total number of sales orders w; obtains the maintenance cost characteristic value and maintenance efficiency characteristic value through data capture and comprehensive calculation; The management feedback module receives the defect density index E1 and review pass rate E2 from the design quality control module; it receives the material quality index E3(j), reliability characteristic parameter E4(j) and residual stress characteristic value of each material type j from the production quality analysis module. Receive the customer maintenance cost characteristic value E5 and the maintenance efficiency characteristic value E6 sent by the customer feedback analysis module; When the defect density index E1 is greater than the preset threshold value E1max or the review pass rate E2 is greater than the preset threshold value E2Max, the product design quality is judged to be poor; When the material quality index E3(j) is less than the preset threshold E3min or the reliability characteristic parameter E4(j) is less than the preset threshold E4max or the residual stress characteristic value When it is greater than the preset threshold, it is determined that the product production material, that is, material type j, has performance problems; When the maintenance cost characteristic value E5 is greater than the preset threshold value E5Max, it is determined that the product quality control is insufficient and the after-sales customer service maintenance cost is too high; When the maintenance effectiveness characteristic value E6 is less than the preset threshold value E6Min, it is determined that the after-sales customer service level is too low.

3. The Internet-based digital quality management platform according to claim 1, characterized in that: The specific process of constructing a 3D geometric model is as follows: Extract the geometric feature information from the six views of the product design drawing in the image file and import it into the finite element analysis software; match the extracted geometric feature information with the target shape based on the shape matching algorithm, build the three-dimensional geometric model of the product and perform image enhancement, mesh repair, shape matching and noise removal processing; Obtain the volume V of the three-dimensional geometric model; Construct an XYZ three-dimensional coordinate system in the three-dimensional geometric model.

4. The Internet-based digital quality management platform according to claim 1, characterized in that: The preset review question types include: Type 1: Review issues for violations of general design specifications; Type 2: Modification suggestions for violations of mandatory provisions; Type 3: Review issues for errors in dimensioning; Type 4: Review issues for potential safety hazards; Type 5: Review issues for confusing structural logic; Type 6: Review issues for unclear text descriptions; Match each type of review issue to a corresponding modification parameter Ki; where i is the number of the review issue.

5. The Internet-based digital quality management platform according to claim 2, characterized in that: The specific process of obtaining the mechanical property characteristic parameters, reliability characteristic parameters and residual stress characteristic values ​​through calculation is as follows: Obtain the yield tensile strength A1j, compressive strength A2j, elongation A3j, Poisson's ratio A4j, elastic modulus A5j and yield strength ratio A6j of the sample corresponding to material j; through the formula Calculate the mechanical property characteristic parameters E3(j) of each material type j; wherein λp is a set of preset weight factors; APj' is a set of preset mechanical property parameter standard values; wherein p = 1, 2, 3, 4, 5, 6.

6. The Internet-based digital quality management platform according to claim 2, characterized in that: The specific process of obtaining reliability characteristic parameters through calculation is: Based on atomic spectroscopy, the impurity component ratio B1 obtained in the chemical composition analysis of the samples corresponding to each material is obtained, and the maximum processing speed B2, maximum processing temperature B3 and minimum processing temperature B4 of the samples corresponding to each material type in the hot processing test are obtained; the reliability characteristic parameter E4(j) of each material type j is calculated by the formula E4(j)=qj×B1×B2×exp(B4-B3); wherein qj is the preset weight factor corresponding to each material type j.

7. The Internet-based digital quality management platform according to claim 2, characterized in that: The specific process of obtaining the residual stress characteristic value through calculation is: Access the experimental records in the X-ray stress tester, and retrieve the diffraction angle θ0(j) of the sample of each material type j in the stress-free state, as well as the diffraction angle θ(j) corresponding to the diffraction peak after the hot working test; through the formula Calculate the residual stress characteristic value of each material type j where A4j is Poisson's ratio, A5j is the elastic modulus, and Ψ is the angle between the measurement direction and the stress direction.

8. The Internet-based digital quality management platform according to claim 2, characterized in that: The specific process of obtaining the maintenance cost characteristic value and maintenance efficiency characteristic value through data capture and comprehensive calculation is as follows: Get the total number of complaints u and number each customer complaint, the number symbol is r, r = 1, 2, 3, ..., u; Obtain the product problem type for each customer complaint, including: technical support issues, packaging issues, quality issues, functional issues, logistics safety issues, and installation issues. Each product problem type corresponds to a preset impact factor g; Obtain the processing time Tr(r) and customer satisfaction Pr(r) after processing for each customer complaint r; Get the first processing and resolution result of each customer complaint r. If the result is to resolve the customer complaint, set the first resolution status code Solve(r) to 1; otherwise, set the first resolution status code Solve(r) to 0. Get the customer's repurchase status after each complaint r. If the customer repurchases within the preset time T after the complaint, set the repurchase status code Buy-Back(r) to 1; otherwise, set the repurchase status code Buy-Back(r) to 0. By formula Calculate the maintenance cost characteristic value E5 and the maintenance effectiveness characteristic value E6.

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